Enhancement of condition monitoring information from the
control data of electrical motors based on machine learning
techniques
Sensors(Data aquazation)
preprocessing
Feature Extraction
Feature selection
Diagnosis/prognosis
Sensors: Therrmal Monitoring-Infrared camera, Thermistors.
Vibration Monitor-Vibration Transducers
Acoustic Emission-Wide band or resonant piezo electric sensors
Electrial Signature Analysis-MCSA sensor (Motor current signature analysis)
Pre-processing: Gear boxes, Centrifugalpumps, Reciprocating compressors - CWT Analysis
Bearing Faults -- Frequency Spectral Subtraction based on DWT and SWT
Centrifugal Pump -- MSB technique for extracting non-linear characteristic of
current signal from the motor driving a centrifugal pump systems under different
impeller faults.
Feature Extraction:
Time Domain Analysis:
Root Mean square
Kurtosis Feature extraction of raw vibration signal collection of time
Crest Factor indexed data collected representing in acceleration ,velocity
Or Proximity based on the type of transducer used to
Collect the signals. Vibration signal are collected from rot
-ating machine using vibration transducers in time domain.
Frequency Domain Analysis:
Discrete fast fourier Transform (DFFT)----In FDA techniques are used to know the information based
On frequency characteristics that are not easily observed in
In time domain. Each sine wave will be presented in spectral
Component.each component of a rotating machine produces a single frequency. However,we do not
often see these produced frequencies individually in the measured signal: we see a summation of the
signals that the sensor measured.The spectrum of the frequency components generated from the time
domain waveforms makes it easier to see each source of vibration.
Wavelet Transformthe raw data as a function of time, also
most widely used for analysising and assessing the acquired signals ,it is limited
Time-Frequency Domain: when analysing the non-stationary and non-linear signals for fault diagnosis. Data-
driven .Techniques such as EMD and ITD allow multi- components of signal to be
Short-time fourier transform
decomposed into different simple intrinsic components where the effective
features can be extracted and used for detecting and diagnosing the faults.
Envelope analysis: STFT: STFT, which computes the DFT by decomposing a signal into shorter
segments of equal length using a time-localised window function.
Empirical mode Decomposition
Wavelet: Wavelet analysis decomposes the signal based on a family of ‘wavelets’.
Intrinsic Time-scale Decomposition
Feature Selection:
Diagnosis:
SVM
Equipment:
Sinocera Ye6232B multi channel for acquiring the experimental data.
Software:YE7600 softwarre where the vibration levels and current
signals could be monitoried and analysed in real time.
Developing Anomaly detection, Diagnostics and prognostics
for condition monitoring with limited historical
data in new application Such a tidal power
Vibration Data
preprocessing
Feature Extraction
Classification
Diagnosis/prognosis
Sensor:
HS1000- High resolution accelerometer Data-DXI accelerometer
Sensor for vibration.
Dta MIniing: To discover trends and relationship b/w parameters with
in low resolution datasets from the HS1000 tidal turbine.
For data mining CRISP-DM process model utilized.
Data Analysis:
Correlation : Discover relationships b/w pairs of variables.
Principal Component Analysis- It is used to reveal the complex
relationship by transforming data into projections along different axis.
Change point analysis: Data parameters change notably overtime used
to define in time series of data or detect changes in data missed by
visual inspection.
Anomaly Detection Techniques:
Probabilistic—GMM,KDE
Distance based—K Means clustering, K Nearest neighbors
Domain Based-One class SVM, Envelop Fitting
Reconstruction Based-curve fitting
Pre-processing:
--Order Tracking
Computed resampling
Vold Kalman filter
Enveloping
Feature Extraction:
Time Domain
RMS
Crest factor
Kurtosis
Skewness
Frequency Domain:
FFT
Time-frequency Domain:
STFT
Wavelet Transform
Cepstrum analysis
Classification:
SVM
Decision Tree
KNN
Feature based diagnostics process
Vibration Data
Vold Kalman filter
RMS
Z-score
Classification(SVM,Decisi
on Tree,KNN)
Prognosis:
Prognosis Method:
-Time To Failure Analysis
- Stressor based
- Degradation based prognostics
Software: Matlab
Wireless sensor Network Platform for Railway Condition
Monitoring
Sensors: MEMS sensors-MEMS accelerometer and geophone.
ADXL 335 Accelerometer sensor.
Pre-processing circuit: NI-3226 sensor node
NI-9791 real time gateway
Feature Extraction:
Frequency analysis- Raw signals analysis is identify like
minimum magnitude and maximum magnitude, peaks,
threshold level.
Frequency analysis of a sinusoidal signal on matlab.
Frequency analysis of the signal for various sampling intervals
in LABVIEW.
Diagnosis:
-Tri-axial acceleration magnitude signal
MATLAB
-Vertical acceleration signal and FFT
Equipment Used:
-MEMS Sensors
-Arduino Liquid,NI-3220,NI-9791
- R-2R ladder CKT
Software: -Labview
-Matlab
- NI-max
Accelerometer sensor
NI-3226 sensor node
NI-9791 real time
gateway
Frequency Analysis
Tri-axial and vertical
acceleration signal and
FFT
Intelligent-Methods for condition monitoring rolling bearings
Using Vibration data
Sensors :
Vibration data from Torsions Sensors-Torque Transducer and
Encoder.
Pre-processing:
Frequency domain:
Compressive sampling for time -frequency
representation using FFT
Feature Extraction:
Time Domain Analysis-
Statistical functions
Time synchronous averaging
Time series regressive models
Filter-based models
Stochastic Parameter techniques
Blind source separation
Frequency Domain Analysis-
DFT
FFT
Frequency spectrum statistical feature
Time-Frequency Domain Analysis-
Wavelet analysis
Short time fourier Transform(STFT)
HHT
EMD
Nigner-vile Distribution
Local Mean Decompoisition
Spectral Kurtosis and Kurtogrsm
Linear subspace Learning-PCA
ICA
LDA
Feature selection:
Filter model-based
Wrapper Model Based
Embedded Model-Based
Diagnosis:
SVM
Health condition on bearings
LRC
ANN
CSFR framework assessment and validation
CS is used to learning the high dimensional data
And combining the efficiency of CS in improving the rolling
bearings condition monitoring.
Methods include-----CSFR framework based methods
CSLSL based methods
CS-SAE-DNN methods
Designing new methods for vibration
based bearing for condition monitoring
Vibration Data
Frequency Domain
Time-frequency
domain( NVD,EMD,HHT,
Linear subspace
Learning)
Embedded Model-
based
SVM,LRC,ANN and CSFR
Hidden Markov Model Supported Machine Learning for
Condition monitoring of DC-link capacitors
Currents measurements
Frequency Domain
FFT
FFT smoothing
SVM ANN
Lamada Lamada
Type equation here .
SVM ANN
SVM,LRC,ANN and CSFR
